code: def generate_dummy_inputs(
      self,
      tokenizer: "PreTrainedTokenizer",
      batch_size: int = -1,
      seq_length: int = -1,
      is_pair: bool = False,
      framework: Optional["TensorType"] = None,
  ) -> Mapping[str, Any]:
      common_inputs = super(
          OnnxConfigWithPast, self
      ).generate_dummy_inputs(
          tokenizer,
          batch_size=batch_size,
          seq_length=seq_length,
          is_pair=is_pair,
          framework=framework,
      )
  
      # We need to order the input in the way they appears in the forward()
      ordered_inputs = OrderedDict(
          {"input_ids": common_inputs["input_ids"]}
      )
  
      # Need to add the past_keys
      if self.use_past:
          if not is_torch_available():
              raise ValueError(
                  "Cannot generate dummy past_keys inputs without PyTorch installed."
              )
          else:
              import torch
  
              batch, seqlen = common_inputs["input_ids"].shape
              # Not using the same length for past_key_values
              past_key_values_length = seqlen + 2
              head_dim = (
                  self._config.hidden_size // self.num_attention_heads
              )
              past_key_shape = (
                  batch * self.num_attention_heads,
                  head_dim,
                  past_key_values_length,
              )
              past_value_shape = (
                  batch * self.num_attention_heads,
                  past_key_values_length,
                  head_dim,
              )
              ordered_inputs["past_key_values"] = [
                  (
                      torch.zeros(past_key_shape),
                      torch.zeros(past_value_shape),
                  )
                  for _ in range(self.num_layers)
              ]
  
      ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
      if self.use_past:
          mask_dtype = ordered_inputs["attention_mask"].dtype
          ordered_inputs["attention_mask"] = torch.cat(
              [
                  ordered_inputs["attention_mask"],
                  torch.ones(
                      batch, past_key_values_length, dtype=mask_dtype
                  ),
              ],
              dim=1,
          )
  
      return ordered_inputs
  
file_path: transformers/src/transformers/models/bloom/configuration_bloom.py
imports: from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, List, Mapping, Optional
  from packaging import version
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import is_torch_available, logging
label_desc: Use image node_id 6 for calling the BloomOnnxConfig obj's underlying member method code with example usage: obj.generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework) and returns: ordered_inputs